Method for Diagnosing Multiple Sclerosis

ABSTRACT

Disclosed is a method for diagnosing multiple sclerosis and more particularly to a method for diagnosing multiple sclerosis by measuring levels of antibodies to glycans in a biological sample.

RELATED APPLICATIONS

This application claims priority to U.S. Ser. No. 60/400,914, filed Aug.2, 2002; U.S. Ser. No. 60/447,076, filed Feb. 13, 2003; U.S. Ser. No.60/462,984 filed Apr. 15, 2003; and U.S. Ser. No. 60/473,231, filed May23, 2003. The contents of these applications are incorporated herein byreference in their entireties.

FIELD OF THE INVENTION

The invention relates generally to a method for diagnosing multiplesclerosis and more particularly to a method for diagnosing multiplesclerosis by measuring levels of antibodies to glycans in a biologicalsample.

BACKGROUND OF THE INVENTION

Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease ofthe central nervous system. It is a common cause of persistentdisability in young adults. In patients suffering from MS, the immunesystem destroys the myelin sheet of axons in the brain and the spinalchord, causing a variety of neurological pathologies. In the most commonform of MS, Relapsing-Remitting, episodes of acute worsening ofneurological function (exacerbations, attacks) are followed by partialor complete recovery periods (remissions) that are free of diseaseprogression (stable). It has been reported that ninety percent ofpatients with MS initially present with a clinically isolated syndromebecause of an inflammatory demyelinating lesion in the optic nerve,brain stem, or spinal cord. About thirty percent of those patients witha clinically isolated syndrome progress to clinically definite MS within12 months after presentation. The subsequent progression of the diseasecan vary significantly from patient to patient. The progression canrange from a benign course to a classic relapsing—remitting, chronicprogressive, or rare fulminant course.

A method for diagnosing MS that facilitates early detection ofclinically definite MS would be valuable for both managing the diseaseand providing counsel for the patient. For example, patients diagnosedearly with clinically definite MS could be offered disease modifyingtreatments that have recently been shown to be beneficial in early MS.

Current methods for assessment and tracking progress of MS are based onassessment and scoring of patients' function in attacks and accumulateddisabilities during the attacks. One assessment used to assess MS is thecommonly used Expanded Disability Status Scale (EDSS). However, EDSS isbased on a subjective assessment of patient function.

Methods for diagnosis can also include tracking brain lesions byMagnetic Resonance Imaging (MRI) or testing Cerebrospinal Fluid (CSF)for Oligo-Clonal Banding (OCB). MRI is a physical method for assessmentof brain lesions and is expensive for routine use. Moreover, thecorrelation between MRI results and disease activity is poor.Cerebrospinal Puncture is an un pleasant invasive procedure that is notsuitable for routine use. In addition, both methods assess damage onlyafter it has occurred; neither method can predict the onset of attacks.A further disadvantage in testing for OCB in CSF and MRI as a way todiagnose MS is that a negative OCB or MRI will not preclude theexistence of MS.

There is a need for a method that uses objectively assessed markers fordiagnosing MS and for predicting the onset of attacks in patientssuffering from MS.

SUMMARY OF THE INVENTION

The invention is based in part on the discovery that MS patients haveelevated serum levels of auto antibodies of IgG, IgA, IgM that bind theglycan structures Glc (α) or Glc (α 1-4) Glc (α) or Glc (α 1-4) Glc (β)as compared to the serum levels of these autoantibodies in healthyindividuals. In addition, the same autoantibodies specific for theseglycan structures are elevated during the exacerbation state as comparedto the level observed in patients in remission and healthy individuals.A high correlation has also been observed between IgM anti-Glc(α)antibody serum levels in females, clinically diagnosed(relapsing-remitting) MS patients, and the women's EDSS (ExpandedDisability Status Scale) score. The high correlation indicates that thelevels of IgM anti-α-Glucose in serum can act as a clinical surrogateendpoint marker for the activity of the disease and a way to track theefficacy of a drug compound in clinical trials.

Monitoring the levels of those antibodies in the blood of MS suspectedpatients facilitates quick and cost effective early diagnosis of MSpatients and early prescribing of disease modifying drugs. Monitoring ofthe levels of those antibodies in the blood of defined MS patients willalso enable quick and cost effective monitoring of the effects ofprescribed drugs, and early detection of attacks, enabling earlyprophylactic treatment.

Among the additional advantages of the invention are that the existenceof MS in patients can be determined at an earlier stage of the disease,when its symptoms may resemble many other MS-like diseases. Earlydiagnosis allows physicians to treat MS earlier in the course of thedisease, thereby minimizing or preventing the damage caused by thedestruction of myelin and disabilities brought about by thisdestruction. In addition, the methods disclosed herein enable physiciansto follow MS patients regularly in order to assess the disease severity,to monitor therapy, and change treatment once signs for coming attacksappear. For example, an increase in biomarkers indicative of an MSattack may warrant administration to the patient of methylpredisone,which is a general anti inflammatory agent commonly administered duringattacks.

The methods disclosed herein can also be used to select the best drugtreatment for a specific patient. For example, a patient may start thetreatment course with a certain drug, and the change in the markerlevels will be indicative for the effectiveness of drug. Reversion ofmarker levels to a diseased state indicates the drug is losingeffectiveness, and the drug can be replaced with a second drug after ashort time period. Otherwise, a physician will have to wait for the nextattacks to determine if the drug is effective for the specific patient.

The biomarkers disclosed herein can additionally act as a surrogate endpoint for assessing the response of a patient to the tested drug in acost effective way. A surrogate end point based on a serological testfacilitates efficient testing of new potential MS drugs.

In one aspect, the invention features a method of diagnosing multiplesclerosis in a subject. The method includes providing a test sample froma subject and detecting in the test sample at least one biomarker thatis an antibody that binds specifically to a glycan structure. Theantibody can be, e.g., an anti-Glc (α) antibody, an anti-Glc (α 1-4) Glc(α) antibody, an anti-Glc (α 1-4) Glc (β) antibody, an anti-Glc (β)antibody, an anti-Gal (β) antibody; an anti-Glc (β 1-4) Glc (β 1-4) Glc(β)antibody, an anti-GlcNAc (β 1-4) GlcNAc (β)antibody, an anti-L-Araf(α)antibody, an anti-L-Rha (α)antibody, an anti-Gal (β 1-3) [GlcNAc (β1-6)] GalNAc (α)antibody, an anti-Gal (β 1-4) GlcNAc (α)antibody, ananti-Gal (β 1-3) GalNAc (α), an anti-Gal (β 1-3) GlcNAc (β), ananti-GlcA (β) antibody, or an anti-GlcA (β) antibody, or an anti-Xyl (α)antibody. The levels of antibody or antibodies in the test sample arecompared to a control sample, which is derived from one or moreindividuals who show multiple sclerosis symptoms and whose that havemultiple sclerosis symptoms with a known multiple sclerosis status, orfrom an individual or individuals who do not show multiple sclerosissymptoms. MS status can include, e.g., exacerbations, attacks,remissions, and stable stages of the disease.

In various embodiments, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17 or 18 of these antibodies are detected. In someembodiments, the antibody detected in the test sample is an anti-Glc(α)antibody, an anti-Glc (α 1-4) Glc (α) antibody or both an anti-Glc(α) antibody and an anti-Glc (α 1-4) Glc (α) antibody.

In some embodiments, the control sample consists essentially of apopulation of one or more individuals that do not show symptoms of amultiple sclerosis. In other embodiments, the control sample consistsessentially of a population who do show symptoms of a multiplesclerosis. The presence of MS in the control sample can be determinedusing techniques known in the art, e.g., an Expanded Disability StatusScale (EDSS) assessment or a Magnetic Resonance Imaging (MRI)assessment, or both.

The test sample can be, e.g., a biological fluid. Examples of biologicalfluids include, e.g., whole blood, serum, plasma, spinal cord fluid,urine, or saliva.

The subject can be either a female or a male.

The antibody detected can be, e.g., an IgM type or an IgA type or an IgGantibody.

In some embodiments, the type of multiple sclerosis detected is earlymultiple sclerosis.

Also provided by the invention is a method of diagnosing a multiplesclerosis exacerbation in a subject. The method includes providing atest sample from a subject and detecting an anti-Glc (α)IgM typeantibody and/or an anti-Glc (α 1-4) Glc (α) IgM type antibody in thetest sample. The levels of the antibody in the test sample are comparedto a control sample, which is derived from one or more individuals whosemultiple sclerosis status is known.

In some embodiments, the control sample consists essentially of apopulation of one or more individuals that do not show symptoms of amultiple sclerosis exacerbation and whose multiple sclerosis status isin remission. A multiple sclerosis exacerbation is diagnosed in thesubject if more anti Glc (α)antibody or anti-Glc (α 1-4) Glc (α)antibodyis present in the test sample than in the control sample. In otherembodiments, the control sample consists essentially of a population ofone or more individuals that show symptoms of a multiple sclerosisexacerbation, and a multiple sclerosis exacerbation is diagnosed in thesubject if levels of anti-Glc (α)IgM type antibody and/or anti-Glc (α1-4) Glc (α)IgM type antibody are present in similar amounts in the testsample and the control sample.

The test sample can be, e.g., a biological fluid. Examples of biologicalfluids include, e.g., whole blood, serum, plasma, spinal cord fluid,urine, or saliva.

The subject can be either a female or a male.

The antibody detected can be, e.g., an IgM type or an IgA or an IgG typeantibody.

In some embodiments, the diagnosis is an early diagnosis of multiplesclerosis exacerbation.

In some embodiments, the subject has been treated with an MS therapeuticagent, e.g., interferon beta or glitamerer acetate administeredsubcutaneously.

Also within the invention is method for assessing multiple sclerosisdisease severity in a subject. The method includes providing a testsample from a subject and determining whether the test sample containsan anti-Glc (α)IgM type antibody and/or an anti Glc (α 1-4) Glc (α)IgMtype antibody. The amount of antibody in the test sample is compared tothe amount of the antibody in the control sample, which is derived fromone or more individuals whose multiple sclerosis disease severity isknown.

In some embodiments, the control sample consists essentially of apopulation of one or more individuals whose multiple sclerosis diseaseseverity is defined by Expanded Disability Status Scale (EDSS), changesin an EDSS score, or a Magnetic Resonance Imaging (MRI) assessment.

The test sample can be, e.g., a biological fluid. Examples of biologicalfluids include, e.g., whole blood, serum, plasma, spinal cord fluid,urine, or saliva.

If desired, the method may further include selecting a therapeutic agentfor treating multiple sclerosis by selecting a therapeutic agent anddosage regimen based on the relative levels of the antibody orantibodies in the test sample and the control sample.

In some embodiments, higher levels of antibodies in the test samplerelative to the control sample indicate selection of a therapeutic agentand dosage regimen that is subcutaneous administration of interferonbeta (BETAFERON®, AVONEX®, REBIF®) or subcutaneous administration ofglitamerer acetate (COPAXONE®).

The subject can be either a female or a male.

Also provided by the invention is a kit for diagnosing symptomsassociated with multiple sclerosis. The kit include a first reagent thatspecifically detects an anti-Glc (α)antibody, a second reagent thatspecifically detects an anti-Glc (α 1-4) Glc (α)antibody, and directionsfor using the kit. The kit optionally includes a reagent thatspecifically detects an IgM type antibody.

Also within the invention are substrates that include reagents thatspecifically detect the antibodies disclosed herein, e.g., an anti-Glc(α) antibody, an anti-Glc (α 1-4) Glc (α) antibody, an anti-Glc (α 1-4)Glc (β) antibody, an anti-Glc (β) antibody, an anti-Gal (β) antibody; ananti-Glc (β 1-4) Glc (β 1-4) Glc (β)antibody, an anti-GlcNAc (β 1-4)GlcNAc (β)antibody, an anti-L-Araf (α)antibody, an anti-L-Rha(α)antibody, an anti-Gal (β 1-3) [GlcNAc (β 1-6)] GalNAc (α)antibody, ananti-Gal (β 1-4) GlcNAc (α)antibody, an anti-Gal (β 1-3) GalNAc (α), ananti-Gal (β 1-3) GlcNAc (β), an anti-GlcA (β) antibody, or an anti-GlcA(β) antibody, or an anti-Xyl (α) antibody. The substrate can be, e.g.,planar.

In a further aspect, the invention provides a method of selecting atherapeutic agent for treating multiple sclerosis. The method includesproviding a test sample from a subject diagnosed with, or at risk for,multiple sclerosis and determining whether the test sample contains ananti-Glc (α) antibody. Levels of the antibody in the test sample to arecompared to levels of antibody in a control sample consistingessentially of one or more individuals whose multiple sclerosis diseaseseverity is known. A therapeutic agent and dosage regimen is selectedbased on the relative levels of the antibody in the subject sample andthe control sample.

In some embodiments, the method further includes determining whether thetest sample contains an anti-Glc (α 1-4) Glc (α)antibody and comparingthe levels of the anti-Glc (α 1-4) Glc (α)antibody in the test sample tolevels of antibody in a control sample consisting essentially of one ormore individuals whose multiple sclerosis disease severity is known

In some embodiments, the control sample consists essentially of one ormore individuals whose status is no multiple sclerosis or stablemultiple sclerosis.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by a person of ordinaryskill in the art to which this invention belongs. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of the present invention, suitable methods andmaterials are described below. All publications, patent applications,patent, and other references mentioned herein are incorporated byreference in their entirety. In the case of conflict, the presentspecification, including definitions, will control. In addition, thematerials, methods, and examples are illustrative only and not intendedto be limiting.

Other features and advantages of the invention will be apparent from thefollowing detailed description and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the decision tree for determining that a MS suspectedpatient actually has MS.

FIG. 2 shows the decision tree for selecting a drug and dose for an MSpatient based on levels of anti Glc (α 1-4) Glc (α) or Glc (α)antibodies.

FIG. 3 shows the decision tree for prediction and early diagnosis ofattacks in MS patients.

FIG. 4 is a table showing the relative fluorescence from binding ofdifferent anti glycan antibodies in MS patients as well as in normalindividuals. The glycans structures are presented in the upper line ofthe table in LINEARCODE® syntax.

FIG. 5 shows the average and median signal for anti glycan antibodies tovarious glycans from sera extracted from MS patients versus normalcontrol sera. The glycans structures are presented in LINEARCODE®syntax.

FIG. 6 is a graph showing the differences between average signals of MSand healthy individuals, bars represents standard deviation. The glycansstructures are presented in LINEARCODE® syntax.

FIG. 7A is a graph showing the average signal from binding of anti Glc(α), (Glycan #11) and Glc (α 1-4) Glc (α), (Glycan #12)IgM in MS andhealthy populations.

FIG. 7B is a graph showing the average signal from binding of anti Glc(α) Glycan #11 and Glc (α 1-4) Glc (α) Glycan #12IgM in MS patients inattack, stable MS patients and healthy populations.

FIG. 8 is a graph showing the correlation between relative fluorescencefrom adhesion of anti Glucose alpha IgM antibodies in anti Glc (α)positive MS patients (left box) negative MS patients (right box) samplesand their EDSS levels.

FIG. 9 is a graph showing the temporal stability of the signal frombinding of IgM, IgG and IgA anti glycan antibodies over 13 weeks in 7healthy individuals.

FIGS. 10A-10E show the glycan array; chemical structure, specificity ofLectin interaction and reproducibility. FIG. 10A shows an p-amino phenylP-saccharide covalently linked at its reducing end to a solid surfacevia a linker.

FIG. 10B shows batch-to-batch reproducibility of binding of biotinylatedWGA to the glycan array. Three separate batches of arrays were assayedsimultaneously with biotinylated WGA

FIG. 10C shows a competition assay with ConA to bound Man (α).Increasing concentrations of soluble Mannose or Gal (β 1-4) Glc wereincubated with biotinylated ConA (1.5 μg/ml) for 1 hr, and detected withStreptavidin conjugated to Europium.

FIG. 10D shows the specificity of lectin binding to different anomers.ConA binding to negative control Glycerol (19), Man (α) (26) and Man (β)(27). GSI binding to -Gal (α) (1), Gal (β) (2), GalNAc (α) (7), andGalNac (β) (8).

FIG. 10E shows plate-to-plate reproducibility of the glycan array. Fiveidentical plates presenting GlcNac (β) were probed with biotinylatedWGA.

FIG. 11 shows the glycan binding profile of a healthy human population.Anti-carbohydrate antibody binding to assorted glycans (see Table 5 forglycan structures) in serum samples from 72 individuals as measured withbiotinylated Protein A. Each dot represents the average of twoexperiments, each done in quadruplicate. The box includes signals of 50%of the population. The thick and thin lines in the box represent themean and median values, respectively. The boundary of the box closest tozero indicates the 25th percentile, and the boundary of the box farthestfrom zero indicates the 75th percentile. Whiskers above and below thebox indicate the 90th and 10th percentiles. The level of nonspecificsignal measured was defined empirically; Glycans against which antibodylevels were found to be relatively low and highly variable betweenexperiments were designated to define background level (not shown). Theaverage signal value for these glycans was calculated and subtractedfrom the signal obtained for each serum sample and particular glycan.The average background was 3×10⁵ RFU. TBST is Tris-buffered Saline withTween-20 (see Experimental Protocol).

FIG. 12 shows the signals of individual sera against a series ofglycans. The anti-glycan antibody binding measured in relativefluorescence units (RFU) were transformed using a histogramequalization-like method which employs a monotonic, non-linear mapping.This way, the RFU values were re-assigned to range between 0 (blue) and255 (red). The data were clustered using a simulated annealingalgorithm.

FIGS. 13A-13C show the binding profile of affinity purified (A)anti-L-Rha (α) (B) anti-GlcNAc (α) and anti-GlcNAc β 1-4) GlcNAc (β) and(C) anti-Glc (β 1-4) Glc (β 1-4) Glc (β) and anti-GlcNAc (β 1-4) GlcNAc(β) antibodies to an array of 33 glycans. The glycans structures aredescribed in Table 5. Amount of antibody bound was measured usingbiotinylated Goat Anti-human IgG antibody.

FIGS. 14A and 14B show the specificity of anti-Glc (β 1-4) Glc (β 1-4)Glc (β) antibody. (A) Competitive inhibition of anti-Glc (β 1-4) Glc (β1-4) Glc (β) antibody binding. Inhibition of binding of affinitypurified anti-Glc (β 1-4) Glc (β 1-4) Glc (β) antibody to p-aminophenyl-β-Glc (β 1-4) Glc (β 1-4) Glc (β) immobilized to the well surfaceas a function of Glc (β 1-4) Glc (β 1-4) Glc or Gal (β 1-4) Glcconcentration. The amount of antibody bound was measured usingbiotinylated Goat Anti-human IgG antibody. (B) Binding of anti-Glc (β1-4) Glc (β 1-4) Glc (β) (A) and anti-L-Rha (α) (B) antibodies to theircognate saccharide after incubation with crystalline or amorphouscellulose. The amount of antibody bound was measured using biotinylatedGoat Anti-human IgG antibody.

FIGS. 15A-15C are graphic representations of bindings of IgG, IgA, andIgM isotypes of healthy individuals to the indicated glycans.

FIG. 16A is a matrix representation of glycans used to examine sera ofatherosclerosis patients suffering from unstable or stable angina.Glycans against which significantly different antibody levels weremeasured in the different patient groups are labeled with filledsquares. Glycans are listed in table 4.

FIG. 16B is a graphic representation showing levels of antibodiesagainst glycans #2 and #29 in the three patient groups; unstable,stable, and non atherosclerotic. The box includes signals from 50% ofthe population. The thick and thin lines in the box represent the meanand median values, respectively. The boundary of the box closest to zeroindicates the 25th percentile, and the boundary of the box farthest fromzero indicates the 75th percentile. Whiskers above and below the boxindicate the 90th and 10th percentiles.

FIG. 17 is a histogram showing the number of samples in the threepatient groups positive for anti-IgA antibodies against glycan #2 orglycan #29.

FIG. 18A is a histogram showing the distribution of antibody levelsagainst glycans #2 and #15 in the three patient groups. The box includessignals from 50% of the population. The thick and thin lines in the boxrepresent the mean and median values, respectively. The boundary of thebox closest to zero indicates the 25th percentile, and the boundary ofthe box farthest from zero indicates the 75th percentile. Whiskers aboveand below the box indicate the 90th and 10th percentiles.

FIG. 18B is a histogram showing the number of samples in the threepatient groups positive for anti-IgA antibodies against glycan #2 orglycan #15.

FIG. 19 is a graphical representation of the specificity and sensitivitybased on anti-IgA antibodies levels against glycan #2, glycan #15,glycan #17, and glycan #49. A—Atherosclerosis; S—Stable; US—Unstable;NA—Non-Atherosclerosis.

FIG. 20 is a histogram showing the binding profile of CD4+ cells from asingle individual to various glycans. Glycan structures represented inLINEARCODE® syntax.

FIG. 21A is a graph showing the median relative fluorescence for CD4+cells from each of the seven individuals Glycan structures representedin LINEARCODE® syntax.

FIG. 21B shows the signals of individual sera against a series ofglycans. The anti-glycan antibody binding measured in relativefluorescence units (RFU) were transformed using a histogramequalization-like method which employs a monotonic, non-linear mapping.This way, the RFU values were re-assigned to range between 0 (blue) and255 (red). The data were clustered using a simulated annealingalgorithm.

DETAILED DESCRIPTION OF THE INVENTION

The methods provided herein allow for early diagnosis of initial andrecurring multiple sclerosis using objectively assessed biomarkerlevels. The current decision tree for diagnosing a patient with MS isdescribed in FIG. 1. A patient with acute worsening of neurologicalfunction initially has to be diagnosed as a defined MS patient beforebeing eligible for treatment with disease modifying drugs. The physicianwill have to determine if the patient has MS like symptoms (such asYounger stroke, Lupus, Vitamin B-12 deficiency, Anti phospholipidsyndrome, Severe Migraine) or if they actually have MS. The patient willhave to experience a second acute worsening of neurological function(attack) before being diagnosed as a MS patient and be able to startchronic treatment with a MS therapeutic agent such as interferon beta orglatiramer acetate.

Currently, physicians are using MRI for the identification of theexistence of brain lesions and/or the testing of Cerebrospinal Fluid(CSF) for Oligo Clonal Banding (OCB). If MRI gives a clear resultregarding the existence of brain lesions or the presence of OCB in theCSF, the physician may start treatment immediately in order to preventsilent brain lesions. A diagnosis of full MS diagnosis is currently madeonly after the second attack. In case MRI does not give a clear resultor there are no OCB in the patients CSF, no MS is diagnosed andtreatment is delayed until following a second attack.

The method disclosed herein can be performed by extracting blood from apatient with acute worsening of neurological function and suspected tohave MS. The method can identify the existence of MS by measuringanti-Glc (α) and anti-Glc (α 1-4) Glc (α) IgM level. If the level of atleast one of these antibodies is significantly higher then the averagelevel of these antibodies in sera of healthy individuals, the patient isdiagnosed as an MS patient without the need to wait for a second attack.In addition, the quick diagnosis allows for treatment to beginimmediately.

One first line of treatment for MS is interferon β (e.g., INFβ-1a andINFβ-1b). The current evaluation of effectiveness and required dosage ofthe drug is based on continued monitoring of several clinical scores.Currently, the EDSS score and its change over time (e.g., by comparingthe difference in the EDSS every 3-6 months) is the main clinicalparameter for disease management. An important component of theassessment is the level of fatigue and depression experienced by thepatient. The fatigue and or depression can be a symptom of MS, as anautoimmune disease, or a side effect from the usage of interferon beta.Identifying the cause of the fatigue is important for managing thetreatment. For example, if the fatigue is a result of a side effect ofthe interferon, the physician will consider lowering the dosage or evenexchanging it for another drug. However, if the fatigue is due to the MSsymptoms, the physician will have to consider increasing the drug dosage(see FIG. 2).

Screening the patient's blood and determining the level of biomarkersdisclosed herein, e.g., the IgM antibodies anti Glc (α) and anti Glc (α1-4) Glc (α) herein allows for accurate monitoring of therapy.Significantly decreases in antibody levels indicates that the patient isresponding well to the given drug.

Early Detection of Attacks

Currently there is no way to predict the onset of attacks in MSpatients. MRI and clinical evaluation of the patients can only revealdamage that has already occurred. Periodical measurement of the level ofa few anti glycan antibodies (for example anti-Glc (α) IgM or anti-Glc(α 1-4) Glc (α) IgM) in the patient's blood according to the methoddescribed herein allows for physicians to identify upcoming attacksbased upon an increase in levels of these antibodies. Levels of theseantibodies are significantly higher in the blood of patients in MSattack situations vs. patients in a stable state (see FIG. 7). Upondetection of an increase in those antibodies, the physician can start anaggressive steroid treatment to reduce the inflammation and preventdamage to the myelin (see FIG. 3).

Also provided herein are methods of identifying and assessingindividuals with atherosclerosis at risk for stable and unstable anginausing antibody biomarkers specific for glycans, as well the use ofimmobilized glycans to detect cells of interest.

Various glycans structures are discussed in this application. Theglycans are presented either in the International Union of Pure andApplied Chemistry (IUPAC) condensed form for nomenclature carbohydraterepresentation or in LINEARCODE® syntax, for linearcode syntaxprincipals see (Banin E. Neuberger Y. Altshuler Y. Halevi A. Inbar O.Dotan N. and Dukler A. (2002) A Noval Liner Code Nomenclature forcomplex Carbohydrates. Trends in Glycoscience and Glycotechnology Vol.14 No. 77 pp. 127-137). Translation of LINEARCODE to IUPACrepresentation is in Table 1. All the glycan structures that discussedin this disclosure, unless mentioned otherwise are connected to in theindicated anomericity α or β through linker to solid phase as describedin FIG. 10 A.

The invention will be illustrated in the following non-limitingexamples.

Example 1 Comparison Between Antiglycan Antibodies in the Serum ofMultiple Sclerosis (MS) Patients and Normal Population

An anti-glycan antibody (Igs) profile was obtained using GlycoChip®arrays (Glycominds, Ltd., Lod, Israel, Cat No. 9100). The arrays wereconstructed using procedures described in WO00/49412. Anti-glycanantibody profiles of 40 multiple sclerosis patients and 40 sex and agedmatched normal blood donors were compared.

All serum samples were tested using GlycoChip® plates (Glycominds Ltd.,Lod, Israel, Cat No. 9100), which was an array of mono andoligosaccharide covalently attached to a reduced volume 384 wells microtiter plate. The mono and oligosaccharides displayed on the array arelisted in FIG. 4. A translation of the LinearCode™ syntax used todescribe glycan structure to IUPAC nomenclature can be found in Table 1.

The sera of healthy volunteers and MS patients volunteers who had signedan informed consent form were collected in evacuated silicon coated gelcontaining tubes (Estar Technologies Cat# 616603GLV). The sera wereseparated from the blood cells and kept frozen in −25° C. until use.They were analyzed in two separate experiments, each repeated twice onseparate days.

Sera from volunteers were diluted (1:20) in TBST dispensed into aGlycoChip® plate using a Tecan Genesis Workstation 200 robot (10μL/well) and incubated 30 min at 25° C. There were 4 repeats for eachglycan and serum sample on the plate.

The plates were washed with 250 μL/well of high salt buffer (0.15M KNapH 7.2, NaCl 2M, MgSO₄ 0.085M, 0.05% Tween20) in an automatic platewasher (Tecan, PowerWasher™). Ten μl/well of biotinylated protein A (ICN62-265), 1 μg/ml in TBST, was dispensed manually and the platesincubated for 30 min at 25° C. The plate was washed again with high saltbuffer.

Streptavidin-conjugated Europium, Wallac, AD0062 (1 μ/ml, 10 μl/well)was added manually followed by incubation for 30 min at 25° C. in thedark. Washing of the plates with the high salt buffer was repeated.Delfia™ enhancement buffer, (Wallac, 730232, 10 μl/well) was added tothe wells and the plates were incubated at least 30 min in the dark. Thefluorescence of the wells was read with Victor 1420 (Wallac) using timeresolved fluorescence settings Emi. 612 nm and Ext. 340 nm.

The profiles of all the tested patients are displayed in FIG. 4. Theupper 40 lines (MS) describe the anti-carbohydrate level of MS samples,and the lower 40 lines (NC) describe the anti-carbohydrate level ofsamples from normal control population. The values presented areabsolute values without background reduction. Since the detection ofbound antibodies was done with biotinylated protein A, which binds toIgG, IgA and IgM., the signal represents the total binding of antibodiesfrom all sub types IgG, IgA and IgM.

A comparison between the average and median values of anti-carbohydrateantibodies in the MS and normal populations reveals significantdifferences between the samples from the MS patients and the samplesfrom the normal population, see FIG. 5. One example of a majordifference observed between the two groups is the average signal to theglycan Ga4 Gb. A t-test showed that the difference is highlystatistically significant (α=0.05; p<0.001). Another example isAb3(GNb6)ANa, (α=0.05; p<0.001). There are significant differencesbetween the medians of signals of MS and normal population regardingantibodies bound to the following glycans: Glc (α), Glc (α 1-4) Glc (α),Glc (α 1-4) Glc (β), Glc (β), Gal (β), Glc (β 1-4) Glc (β 1-4) Glc (β),GlcNAc (β 1-4) GlcNAc (β), L-Araf (α), L-Rha (α), Gal (β 1-3) [GlcNAc (β1-6)] GalNAc (α), Gal (β 1-4) GlcNAc (α), Gal (β 1-3) GalNAc (α), Gal (β1-3) GlcNAc (β), GlcA (β), GlcA (β), Xyl (α). The signal from boundantibodies in MS group is higher then the signal in the normal controlgroup.

FIG. 6 presents the difference between the average binding values ofanti-glycan antibodies between the populations.

Example 2 Differences in the Levels of Anti-Glc (α), and Anti-Glc (α1-4) Glc (α), a IgM Antibodies in the Serum Between Ms Patients inAttack, Stable Ms Patients and Healthy Population

A glycan array was used to search for biomarkers among the human serumglycan binding antibody repertoire to differentiate between a healthypopulation and a group of Multiple Sclerosis (MS) patients, and betweenMS patients in exacerbation and remission stages. This exampledemonstrates that two IgM antibodies, anti-Glc (α) and anti-Glc (α 1-4)Glc (α), are found at significantly higher levels in MS patients than inhealthy people (sensitivity and specificity of 60% and 93%,respectively), and in MS patients in an exacerbation stage relative topatients in a remission stage (sensitivity and specificity of 89% and71%, respectively). Also provided is an anti-glycan antibody profile fora healthy population, including a range of variation during a 13 weekinterval.

The temporal stability of antiglycan antibodies profile over 13 week inapparently healthy individuals was high. The low levels and of anti-Glc(α) and anti-Glc (α 1-4) Glc (α) IgM in a normal population, and theirhigh level in MS patients, and the high temporal stability of antiglycan antibodies suggests that this anti Glc (α) and anti-Glc (α 1-4)Glc (α)IgM can serve as biomarker for early diagnosis, early prescribingof drugs, monitoring drug effects and early detection of attacks.

All serum samples were tested using GlycoChip® (Glycominds Ltd., Lod,Israel). The glycans were covalently bound to the plastic surfacethrough a linker as previously described (WO02/064556). A listdescribing the mono- and oligosaccharides tested is provided in Table 1.

Blood samples were obtained from apparently healthy blood donors underan informed consent protocol approved by the Helsinki Human StudiesEthical committees of the Belinson Medical Center in Tel-Aviv, Israel,and Carmel Medical Center in Haifa, Israel. Blood samples were collectedfrom MS patients admitted to the Multiple sclerosis Clinic in CarmelMedical Center in Haifa, Israel. The blood samples were collected inevacuated silicon coated tubes containing gel for the separation of serafrom the blood clot (Estar Technologies). After coagulation of theblood, serum was separated by centrifugation and collected. Samples werestored frozen at −25° C. until used.

The volume of all solutions added to the glycan array was 10 μl/well.The sera were diluted (1:20; saturating concentration) in 0.15M Tris-HClpH 7.2, 0.085M Mg₂SO₄, 0.05% Tween 20 (TBST) containing 1% BSA (Sigma),dispensed into glycan array plates using a Tecan Genesis Workstation 200automated handling system, and incubated for 60 min at 37° C. The plateswere then washed with 250 μL/well Phosphate buffered Saline with 0.05%Tween 20 (PBST, Sigma) in an automatic plate washer (Tecan,PowerWasher™). At this point the following reagents, diluted in TBSTwith 1% BSA, were added using a Multidrop 384 dispenser (ThermoLabsystems) and incubated for 60 min at 37° C.: for IgG, IgA, and IgMdetermination—the respective sub-class specific biotinylated goatanti-human Ig antibody (Jackson, Pa., USA) at 2.8 μm/ml, 3 μg/ml, and0.9 μg/ml, respectively; for total Ig determination—biotinylated ProteinA 1 μg/ml, ICN Biomedicals). Following washing with PBST,Streptavidin-conjugated europium (0.1 μg/ml) diluted in TBST with 1% BSAwas added to each well followed by incubation for 30 min at 37° C. inthe dark, and washing with PBST. Delfia™ enhancement solution was thenadded to the wells and the plates were incubated for 30 to 45 min in thedark at room temperature. The fluorescence of the wells was read with aVictor 1420 (Wallac, Finland) plate reader using time resolvedfluorescence settings of 340/612 nm (Excitation/Emission).

Differences in the Levels of Anti-Glc (α) and Glc (α 1-4) Glc (α) a IgMAntibodies in the Serum Between MS Patients in Attack, Stable MSPatients and Healthy Population.

Serum samples were obtained from MS patients admitted to an outpatientclinic for regular examination after they signed informed consent forms.The patient group was 80% female, approximately reflecting the genderratio in the general MS population. In accordance with published data(Ritchie et al., J. Clin. Lab. Anal. 12:363-70, 1998), significantlyhigher levels of IgM (but not IgG or IgA) antibodies were observed insera from both healthy and MS women as compared to men (not shown). Theanalysis was therefore limited to the female MS and healthysub-populations only. Sera of MS patients were initially screened on 54glycans (Table 1) for the presence of IgG, IgM and IgA anti-glycanantibodies with the purpose of identifying markers that would confirmpatients with single acute demyelinating events as MS, and markers thatwould distinguish between patients during the exacerbation and remissionstages of the disease. The experiment was repeated twice using five outof the 54 glycans against which some differences between the groups werefound in the initial round.

A reproducible and statistically significant difference in the levels ofIgM anti Glc (α) and anti-Glc (α 1-4) Glc (α)antibodies was foundbetween the healthy and MS groups (FIG. 7A), but no significantdifferences in IgG or IgA levels were found in these studies (notshown). In sera of both groups of MS patients the levels of IgM anti-Glc(α) and anti-Glc (α 1-4) Glc (α) were significantly higher than in thehealthy population. An arbitrary set optimal cut-off value (the 97%percentile signal of the “healthy” population) was used to identifypositive samples above—and negative samples—below the cut-off value.Thus, anti-Glc (α) binding signals identified correctly 19 out of 42 MSsamples (45% sensitivity) and 42 out of 44 apparently healthy serasamples (96% specificity). Measurement of anti-Maltose bindingidentified correctly 48% of the MS sera and 95% of the apparentlyhealthy sera samples. Defining positive as a sample which signal isabove the cut-off value in either the anti-Glc (α) or Glc (α 1-4) Glc(α) assays, improves the sensitivity to 60%, and leaves specificity at93% (Table 2). The differential distribution of anti-Glc (α) andanti-Glc (α 1-4) Glc (α) antibodies in patients during the exacerbationand remission stages of the disease was significantly higher levels inthe former group (FIG. 7B). No difference was found between untreatedpatients or patients treated with interferon-β (not shown). Using as acut-off of the 80% percentile of the “stable” MS population, it wasdetermined that anti-Glc (α)binding signals identified correctly 15 outof 18 “attack” samples (83% sensitivity), 19 out of 24 “stable” samples(79% specificity relating to stable as symptom free), and 42 out of 44“healthy” samples (95% specificity). Measurement of anti-Maltose bindingidentified correctly 72% of the attack sera, 79% of the “stable” sera,and 97% of the “healthy sera”. Defining a positive as a sample whichsignal is above the cut-off value in either the Glc (α)OR Maltoseassays, results in sensitivity of 89%, and specificity of 71% and 95%relative to “stable” or “healthy” samples, respectively (Table 3). Thehigh specificity and sensitivity of the anti-Glc (α) and anti-Glc (α1-4) Glc (α) IgM antibodies make them an efficient tool for earlydiagnosis and definition of MS patients. The fact that the levels ofthese antibodies in MS attack situation are much higher then in stablesituation make them a tool for early identification and prediction ofattacks in relapsing remitting MS patients.

A high correlation between IgM anti-Glc (α)antibody serum levels infemale, clinically diagnosed (relapsing-remitting) MS patients, whodefined positive for having IgM anti-Glc (α)antibody (as describedabove), and the women's EDSS (Expanded Disability Status Scale) scorewas observed, see FIG. 8, left box. There was no correlation betweenEDSS and the IgM anti-Glc (α)antibody levels in serum for females,clinically diagnosed (relapsing-remitting) MS patients, who definednegative for having IgM anti-Glc (α) antibody, see FIG. 8 left box. Thehigh correlation indicates that the levels of IgM anti-Glc (α) in serumcan act as a molecular surrogate biomarker for evaluation the activityof the disease.

Temporal Range of Anti-Glycan Antibody Levels

When considering any biological parameter for the use as a surrogatebiomarker, it is obviously a prerequisite that the biomarker is notvariable in time in the normal population. Thus, the serum levels ofIgG, IgA, and IgM anti-L-Rha (α), anti-GlcNAc (α), and -anti Glc (β 1-4)Glc (β 1-4) Glc (β) (β Cellotriose), antibodies in seven healthyvolunteers were followed for 13 weeks (FIG. 9). In general, the serumantibody concentrations were found to vary between the differentindividuals, but to be quite stable over time. For example, sera #9161and #9162 have extremely high and temporally stable relative levels ofIgA anti-GlcNAc (α) and Glc (β 1-4) Glc (β 1-4) Glc (β)antibodies,respectively, but relatively normal levels of IgA anti L-Rha(α)antibodies and IgG and IgM antibodies. When changes in antibody leveldo occur they are frequently gradual and continue over several weeks(e.g. serum #9162; IgA anti-Glc (β 1-4) Glc (β 1-4) Glc (β)), but canalso be sudden, e.g. serum #9172; IgM anti-L-Rha (α), which suddenlyincreases between week four and five and then again slowly returns toits basic level.

Example 4 Anti-Glycan Antibody Profile (AGAP) in a Normal HumanPopulation

Total Ig antibody binding (as detected with Protein A) of 72 individualsera to 34 mono- and oligosaccharides (FIG. 11 and Table 5), and IgG,IgA, and IgM binding of 200 sera to six mono- and oligosaccharides (FIG.15A-C) was determined. The strongest signals were recorded forantibodies against GlcNAc (α) and L-Rha (α), while lower levels wereobserved against β4-linked oligosaccharides of glucose, GlcNAc (β),GlcNAC (β 1-4) GlcNAC (β), Gal (α) and Gal (a 1-3) Gal (β 1-4) GlcNAc(β). This is in good agreement with previously published data showingthe distribution of anti-glycan antibodies in a commercially availablehuman serum pool (WO02/064556). The AGAP of subclasses IgG and IgA weresimilar to the total Ig AGAP, while that of IgM was lower and moreuniform among the different glycans. The anti-glycan antibodies of thepopulation tended to fit a lognormal distribution see FIG. 15A-15C. Itis evident that considerable variation in anti-glycan antibody levelsexists between individuals within the population examined, a fact thatsuggests the existence of individual AGAPs, but limits the search ofmarkers to anti-glycan antibodies present at low amounts.

Glycans immobilized on beads affinity beads have also been used topurify antibodies to β4-linked oligosaccharides of glucose and L-Rha(α).Their binding profile and specificity are described in FIGS. 13, 14A and14B.

Example 8 Use of Anti-Glycan Antibodies to Differentiate Between HighRisk Atherosclerosis Patients with Vulnerable Plaques and Low RiskAtherosclerosis Patients with Stable Plaques

Levels of anti-glycan antibodies in the sera of atherosclerosis patientswith vulnerable plaques were compared to levels of glycan antibodies inserum of atherosclerosis patients with stable plaques, as well asindividuals without atherosclerosis.

Atherosclerosis is a major cause of morbidity and mortality in developedcountries. It is a systemic disorder of blood vessel walls that leads tothe development of atherosclerotic plaques on the blood vessel walls.Some of these plaques later become vulnerable to rupture, causing bloodclots leading to heart attacks or stroke.

The main components of atherosclerotic plaques are proteoglycans,lipids, muscle cells, and white blood cells (T-cells and macrophages).In addition, atherosclerosis is perceived as an autoimmune disease whereone of its initiators is cross reactivity between antibodies tobacterial antigens and the antigens on blood vessel walls.

An important point in the development of atherosclerosis is the shiftfrom Stable Plaques (SP), which are associated with low risk, toinflamed Vulnerable Plaques (VP), which are associated with high risk.Differentiating between SP and VP is clinically problematic, as aconclusive distinction can be made only a by post-mortem autopsy.

Serum samples where supplies by Dr. Jacob George from the cardiologydepartment in the Tel Aviv Medical Center, Israel. All patients werenon-diabetic males with an age range from 30 to 69. 39 serum samples ofpatients from the following types were tested:

Unstable Angina—13 Atherosclerosis patients characterized as havingAcute Coronary Syndromes (Q wave or non Q wave myocardial infarctions).Both are considered to develop from rupture of vulnerable plaques.Members of the Unstable Angina group included acute coronary syndromepatients admitted with chest pain and ECG changes or cardiac markerelevation. They complained of recent onset (<3 days) of angina and weresubjected to continued electrocardiogram (ECG) telemetric monitoringduring admission. At least one episode of rest angina or an episodelasting more then 20 min during last 48 hr was detected, along with anincrease in creatine kinase, MB levels or Troponin levels. Members ofthis group had undergone coronary angiography (catheterization), whichdocumented the presence of coronary atherosclerosis.

Stable Angina—13 Atherosclerosis patients were characterized as havingStable Angina. Members of the Stable Angina group had undergone coronaryangiography (catheterization) documenting the presence of coronaryatherosclerosis. No ECG changes were detected, nor were increases increatine kinase, MB levels or Troponin levels detected.

No plaques—13 Patients with normal coronary arteries. Members of the “NoPlaques” group showed no evidence of coronary atherosclerosis followingcatheritization.

An anti-glycan antibody profile was obtained using GlycoChip™ arrays(Glycominds, Ltd., Lod, Israel, Cat No. 9100) constructed usingprocedures described in WO00/49412. All sera samples were tested usingGlycoChip™ plates (Glycominds Ltd., Lod, Israel, Cat No. 9100), whichcontained an array of covalently attached mono and oligosaccharide in areduced volume 384 well micro titer plate. The list of the mono andoligosaccharide displayed on the array as well as their serial numbersare described in Table 4.

Sera were diluted (1:20) in TBST dispensed into a GlycoChip™ plate usinga Tecan Genesis Workstation 200 robot (10 μL/well) and incubated 30 minat 25 degrees Celsius. Each glycan and serum sample on the plate wastested 8 times.

The plates were washed with 250 μL/well of high salt buffer (0.15M KNapH 7.2, NaCl 2M, MgSO4 0.085M, 0.05% Tween20) in an automatic platewasher (Tecan, PowerWasher™) Ten μl/well of biotinylated goat anti-humanIgG, IgM or IgA (Jackson, Pa., USA), 1 μg/ml in TBST, was dispensedmanually and the plates incubated for 30 min at 25° C. The plate waswashed again with high salt buffer.

Streptavidin-conjugated Europium, Wallac, AD0062 (1 μ/ml, 10 μl/well)was added manually followed by incubation for 30 min at 25° C. in thedark. Washing of the plates with the high salt buffer was repeated.Delfia™ enhancement buffer, (Wallac, 730232, 10 μl/well) was added tothe wells and the plates were incubated at least 30 min in the dark. Thefluorescence of the wells was read with Victor 1420 (Wallac) using timeresolved fluorescence settings Emi. 612 nm and Ext. 340 nm.

The glycan binding signal obtained for the “No plaque” group was used tocalculate cut-off values for each glycan above which patients wereconsidered to be positive. These cut-off values were defined as theaverage signal of the “No plaque” group plus one or two standarddeviations. According to this definition a number of glycans wereidentified which had some degree of separating power between the patientgroups (see below). “Separation” based on a certain glycan was definedas at least 50% (7/13) positive samples in the “Unstable angina” or“Stable angina” groups, and 2 or less positive samples in the “Noplaque” group.

FIG. 16A is a matrix representation of glycans used to examine sera ofpatients suffering from unstable or stable angina, the glycan structuresare described in Table 4. Glycans against which significantly differentantibody levels were measured in the different patient groups arelabeled with filled squares. At the cut-off level of average plus twostandard deviations “Separation” was achieved with IgA binding to twodifferent glycans. One separated between IgG antibodies, but noneseparated between IgM antibodies.

The single glycans giving the best separations are presented below:

Unstable Stable No Glycans Result Angina Angina Plaque Ab Positives 7 50 Negatives 6 8 13 Fb Positives 1 9 1 Negatives 12 4 12

Some glycans that were not defined as “separating” still gave somedegree of separation. When used in combinations, separation could beimproved beyond that of the single glycans. The glycans are presentedbelow

Glycans Unstable Stable No LinearCode Result Angina Angina Plaque Aapositives 6 2 0 negatives 7 11 13 Xb positives 1 6 0 negatives 12 7 13Fa positives 5 3 1 negatives 8 10 12 A[3S]b positives 1 6 1 negatives 127 12 GNb4GNb positives 5 0 1 negatives 8 13 12

FIG. 16B is a graphic representation showing levels of antibodiesagainst glycans #2 and #29 in the three patient groups. The box includessignals of 50% of the population. The thick and thin lines in the boxrepresent the mean and median values, respectively. The boundary of thebox closest to zero indicates the 25th percentile, and the boundary ofthe box farthest from zero indicates the 75th percentile. Whiskers aboveand below the box indicate the 90th and 10th percentiles.

FIG. 17 is a histogram showing the number of samples in the threepatient groups positive for anti-IgA antibodies against glycan #2 orglycan #29.

FIG. 18A is a histogram showing the distribution of antibody levelsagainst glycans #2 and #15 in the three patient groups. The box includessignals of 50% of the population. The thick and thin lines in the boxrepresent the mean and median values, respectively. The boundary of thebox closest to zero indicates the 25th percentile, and the boundary ofthe box farthest from zero indicates the 75th percentile. Whiskers aboveand below the box indicate the 90th and 10th percentiles.

FIG. 18B is a histogram showing the number of samples in the threepatient groups positive for anti-IgA antibodies against glycan #2 orglycan #15. At the cut-off level of average plus one standard deviation,“Separation” was achieved with IgA binding to 6 different glycans. IgGand IgM antibody levels were not different in the three groups.

The separation obtained with combinations is shown below (Aa was usedbecause the number of positive sample in the “Stable Angina” group waslower than using Ab, thus improving separation vis-à-vis the “UnstableAngina” group):

Glycans Unstable Stable No LinearCode Result Angina Angina Plaque Aa andPositive with one 8 2 1 GNb4GNb of the glycans Negative with 5 11 12both Aa and Positive with one 8 5 0 Ga4Ga of the glycans Negative with 58 13 both

The specificity and sensitivity of the test to detect “Unstable angina”using Aa and GNb4GNb was thus 62% (8/13) and 88% (23/26), respectively.

A combination of three glycans, Aa, GNb4GNb, and Fb made it possible todetermine specificity also for the “Stable angina” group 75% (9/13).This stems from the fact that Fb detects mostly “Stable angina”. Thespecificity and sensitivity of the combined assay are summarized in FIG.19.

These results demonstrate that a combination of glycans (Gal (α), GlcNAc(β 1-4) GlcNAc (β) and Fu(β) can be used to successfully distinguishbetween stable and unstable angina populations with a specificity of 62%and sensitivity of 88%. This results show that it is possible to developa biomarker based on glycan binding IgA antibodies that distinguishesbetween Unstable and Stable Angina patients.

Example 9 Use of Anti-Glycan Antibodies to Differentiate Between HighRisk Atherosclerosis Patients with Vulnerable Plaques and Low RiskAtherosclerosis Patients with Stable Plaques

Levels of anti-glycan antibodies in the sera of atherosclerosis patientswith vulnerable plaques were compared to levels of glycan antibodies inserum of atherosclerosis patients with stable plaques, as well asindividuals without atherosclerosis.

Atherosclerosis is a major cause of morbidity and mortality in developedcountries. It is a systemic disorder of blood vessel walls that leads tothe development of atherosclerotic plaques on the blood vessel walls.Some of these plaques later become vulnerable to rupture, causing bloodclots leading to heart attacks or stroke.

The main components of atherosclerotic plaques are proteoglycans,lipids, muscle cells, and white blood cells (T-cells and macrophages).In addition, atherosclerosis is perceived as an autoimmune disease whereone of its initiators is cross reactivity between antibodies tobacterial antigens and the antigens on blood vessel walls.

An important point in the development of atherosclerosis is the shiftfrom Stable Plaques (SP), which are associated with low risk, toinflamed Vulnerable Plaques (VP), which are associated with high risk.Differentiating between SP and VP is clinically problematic, as aconclusive distinction can be made only a by post-mortem autopsy.

Serum samples where supplies by Dr. Jacob George from the cardiologydepartment in the Tel Aviv Medical Center, Israel. All patients werenon-diabetic males with an age range from 30 to 69. 72 serum samples ofpatients from the following types were tested:

Unstable Angina—24 Atherosclerosis patients characterized as havingAcute Coronary

Syndromes (Q wave or non Q wave myocardial infarctions). Both areconsidered to develop from rupture of vulnerable plaques. Members of theUnstable Angina group included acute coronary syndrome patients admittedwith chest pain and ECG changes or cardiac marker elevation. Theycomplained of recent onset (<3 days) of angina and were subjected tocontinued electrocardiogram (ECG) telemetric monitoring duringadmission. At least one episode of rest angina or an episode lastingmore then 20 min during last 48 hr was detected, along with an increasein creatine kinase, MB levels or Troponin levels. Members of this grouphad undergone coronary angiography (catheterization), which documentedthe presence of coronary atherosclerosis.

Stable Angina—24 Atherosclerosis patients were characterized as havingStable Angina.

Members of the Stable Angina group had undergone coronary angiography(catheterization) documenting the presence of coronary atherosclerosis.No ECG changes were detected, nor were increases in creatine kinase, MBlevels or Troponin levels detected.

No plaques—24 Patients with normal coronary arteries. Members of the “NoPlaques” group showed no evidence of coronary atherosclerosis followingcatheritization.

An anti-glycan antibody profile was obtained using GlycoChip™ arrays(Glycominds, Ltd., Lod, Israel, Cat No. 9100) constructed usingprocedures described in WO00/49412. All sera samples were tested usingGlycoChip™ plates (Glycominds Ltd., Lod, Israel, Cat No. 9100), whichcontained an array of covalently attached mono and oligosaccharide in areduced volume 384 well micro titer plate. The list of the mono andoligosaccharide displayed on the array as well as their serial numbersare described in Table 4.

Sera were diluted (1:20) in TBST dispensed into a GlycoChip™ plate usinga Tecan Genesis Workstation 200 robot (10 μL/well) and incubated 30 minat 25 degrees Celsius. Each glycan and serum sample on the plate wastested 8 times.

The plates were washed with 250 μL/well of high salt buffer (0.15M KNapH 7.2, NaCl 2M, MgSO4 0.085M, 0.05% Tween20) in an automatic platewasher (Tecan, PowerWasher™) Ten μl/well of biotinylated goat anti-humanIgA (Jackson, Pa., USA), 1 μg/ml in TBST, was dispensed manually and theplates incubated for 30 min at 25° C. The plate was washed again withhigh salt buffer.

Streptavidin-conjugated Europium, Wallac, AD0062 (1 μ/ml, 10 μl/well)was added manually followed by incubation for 30 min at 25° C. in thedark. Washing of the plates with the high salt buffer was repeated.Delfia™ enhancement buffer, (Wallac, 730232, 100/well) was added to thewells and the plates were incubated at least 30 min in the dark. Thefluorescence of the wells was read with Victor 1420 (Wallac) using timeresolved fluorescence settings Emi. 612 nm and Ext. 340 nm.

The cut off was calculated from the 80th percentile of the normalpopulation. According to this definition a number of glycans wereidentified which had some degree of separating power between the patientgroups (see below). “Separation” based on a certain glycan was definedas at least 50% (12/24) positive samples in the “Unstable angina” or“Stable angina” groups, and 5 or less positive samples in the “Noplaque” group.

Glycan Unstable Stable No LinearCode Angina Angina plaques Ga4GaPositives 12 2 5 % Positives 52 8 21 Gb Positives 19 10 5 % Positives 8342 21 ANa Positives 13 8 5 % Positives 57 33 21 ANb Positives 15 8 5 %Positives 65 33 21 GNb4GNb Positives 13 8 5 % Positives 57 33 21 XaPositives 21 7 5 % Positives 100 29 21

These results demonstrate that a combination of glycans Glc (α 1-4) Glc(α), Glc (β), GalNAc (α), GalNAc (β), GlcNAc (β 1-4) GlcNAc (β) andXylose (α) can be used to successfully distinguish between stable andunstable angina populations. This results demonstrate that it ispossible to develop a biomarker based on glycan binding IgA antibodiesthat distinguishes between Unstable and Stable Angina patients.

Example 10 Binding of CD4+ Cells to a Plurality of Glycans Immobilizedon a Solid Substrate

Binding was examined of CD4+ cells from 7 healthy individuals to 47different glycans fragments immobilized on a microarray.

Materials and Methods

20 ml of fresh blood from each of the 7 individuals was drawn using 10ml EDTA-Vaccutainers. Peripheral cell samples were centrifuged (230×g,900 RPM, 10 minutes at room temperature). The plasma was then separatedand the top 2 ml of the cellular fraction transferred to a 15 ml tube.For enrichment of the CD4+ cells, 100 μl RosetteSep reagent was added tothe tubes and incubated at room temperature for 20 minutes. The sampleswere then diluted two-fold in PBS/2% FCS and 5 ml Ficoll is layeredunder the cell suspension using a glass Pasteur pipette.

Tubes were centrifuged for 30 minutes at room temperature, 2400 RPM(˜700×g) with the centrifuge brake off. After centrifugation, tubes werecarefully removed from centrifuge. The upper layer was gently drawn offusing a sterile pipette, leaving the lymphocyte layer undisturbed at theinterface. Using a sterile pipette the leukocyte fraction wastransferred to a clean tube, and tube was completely filled with PBS/2%FCS. The cells were washed twice again by centrifugation for 10 minutes,230×g (1000 RPM) and re-suspended in PBS/2% FCS. Followingcentrifugations, cells are re-suspended in 500 μl RPMI/1640 2% FCS.

Cells were diluted in Türk solution 1:10 and counted. After counting,cells were diluted to a density of 5×10⁶ cells/ml in RPMI/1640 2% FCS,then plated in 24 well plate, 1 ml/well. Cell suspensions are incubatedover night in 95% humidity, 37° C., 5% CO₂ incubator.

To determine cell separation yields by FACS sepearation, 250,000 cellswere suspended in 1 ml FACS buffer and then centrifuged 10 minutes at2000 RPM, 4° C. The supernatant was decanted. The cells werere-suspended in 500 FACS buffer and labeled with 5 μl of anti-CD4antibody. Cells were incubated for 30 minutes on ice, covered fromlight. 1 ml of ice cold FACS buffer was added and centrifuged 10minutes, 2000 RPM, 4° C. Cells were then re-suspended in 300 μl FACSbuffer, stored on ice and scored on a FACS machine.

GlycoChips were placed in slide holders in plastic vessels embedded withmoist paper to retain humidity. Cell suspensions were plated at 1.2μl/well on the GlycoChip, then incubated in 5% CO₂ incubator (95%humidity, 37° C.) for one hour. After incubation, slides were gentlyplaced up-side down in centrifugation chamber immersed in PBS.GlycoChips were centrifuged for two minutes at 700 RPM (minimal g force,˜50×g). Slides were observed by microscope and fixed in PBS/3.7%formaldehyde at room temperature for at least 30 minutes. Slides werethen washed gently ×3 in DDW and air dried.

Propidium iodide solution was prepared in PBS and plated at 1.2 μl/well.GlycoChips were incubated under humid conditions for 15 minutes thengently rinsed ×3 by dipping in DDW. Slides were air dried in the darkand scanned at propidium iodide settings Ext. 535 nm Emi. 655 nm on anarray scanner. The image was analyzed and cell densities weredetermined.

Results

The glycans and controls used for the binding studies are shown in FIG.20, below. The structure are written in Linear Code™ syntax, see Table 1for translation.

A histogram showing the binding profile of CD4+ cells from a singleindividual to various glycans is shown in FIG. 20. Shown is binding inDLU/mm² for each of the glycans or controls is indicated. CD4+ cellbinding to glycans or controls from the seven individuals is shown inFIG. 21A. FIG. 21A shows the median relative fluorescence for CD4+ cellsfrom each of the seven individuals.

These results demonstrate that binding of CD4+ cells varies between thevarious glycans. The strongest binding was observed to the followingglycans, in their order of relative affinity: CD4+ cells bind thefollowing glycans, presented in LinearCode;NNa3Ab4(Fa3)GNb>Mb4Gb>GNb4GNb>Ma3Ma>Ab6Ab. Binding of CD4+ cells toglycans with terminal mannose residues or with Sialyl Lewis X residueswas also detected. Variation of CD4+ binding to particular glycans wasalso detected among the various individuals.

Other Embodiments

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

Tables and Figures:

TABLE 1 Saccharides displayed on the glycan array Common Glycan IUPACLINEARCODE ® Name 0 pNP-OH pNP-0 1 Gal (α) Aa 2 Gal (β) Ab 3 Gal (β 1-3)GalNAc (α) Ab3ANa 4 Gal (β 1-3) GlcNAc (β) Ab3GNb 5 Gal (β 1-4) Glc (β)Ab4Gb Lactose 6 Gal (β 1-6) Gal (β) Ab6Ab 7 GalNAc (α) ANa 8 GalNAc (β)ANb 9 Fuc (α) Fa 10 Fuc (β) Fb 11 Glc (α) Ga 12 Glc (α 1-4) Glc (α)Ga4Ga Maltose 13 Glc (α 1-4) Glc (β) Ga4Gb 14 Glc (β) Gb 15 Glc (β 1-4)Glc (β) Gb4Gb Cellobiose 16 Glc (β 1-4) Glc (β 1-4) Gb4Gb4Gb CellotrioseGlc (β) 17 Glc (β 1-4) Glc (β 1-4) Gb4Gb4GbGb4Gb Cellopentaose Glc (β1-4) Glc 18 Glycerol Glycerol 19 GlcNAc (α) GNa 20 GlcNAc (β) GNb 21GlcNAc (β 1-3) GalNAc (α) GNb3ANa 22 GlcNAc (β 1-4) GlcNAc (β) GNb4GNbChitobiose 23 L-Rha (α) Ha 24 GalA (β) Lb 25 Man (α) Ma 26 Man (β) Mb 27Neu5Ac (α) NNa 28 L-Araf (α) Ra 29 GlcA (β) Ub 30 X(α) Xa 31 X(β) Xb 32Gal (β1-3) [GlcNAc Ab3(GNb6)ANa (β1-6)] GalNAc (α) 33 Gal (β 1-4) GlcNAc(α) Ab4GNa 34 Gal (α1-3) Gal (β 1-4) Aa3Ab4GNb Linear B-2 GlcNAc (β) 35Gal (β1-3) Gal (β1-4)GalNAc (β) Ab4GNb N-Acetyl 36 Man (β1-4) GlcNAc (β)Mb4Gb 37 GlcNAc (β1-6)GalNAc (α) GNb6ANa 38 Fuc (α 1-2) Gal (β) Fa2Ab 39Neu5Ac (α2-3) Gal (β 1-4) NNa3Ab4(Fa3)GNb Sialyl Lewis X [Fuc (α 1- 40Man (α 1-3) Man (α) Ma3Ma 41 GlcNAc (β) 6-sulfate GN[6S]b 42 Glc (β 1-3)Glc (β) Gb3Gb 43 Gal(β) 3-sulfate A[3S]b 44 Neu5Ac (α1-3) Gal (β 1-4)NNa3Ab4GNb Sialyl lactosamine GlcNAc (β) 45 Man (α 1-3) [Man (α1-6)]Ma3(Ma6)Mb Man (β) 46 Neu5Ac (α1-3) Gal (β 1-4) NNa3Ab4Gb Sialyl lactoseGlc (β) 47 GlcNAc (β 1-3) Gal (α1-4) GNb3Ab4Gb Lacto-3 Glc (β) 48 Gal(α1-4) Gal (β 1-4) Glc Aa4Ab4Gb Pk antigen (β) 49 Neu5Ac (α1-6) Gal (β1-4) NNa6Ab4GNb GlcNAc (β) 50 Gal (a 1-4) [Fucp (a 1-3)] Ab4(Fa3)GNbLewis X GlcNAc (b) 51 Neu5Ac (α1-3) Gal (β 1-4) NNa3Ab3(Fa4)GNb SialylLewis A [Fuc (α1-3] 52 Man (α 1-6) Man α Ma6Ma 53 Neu5Ac (α1-3) Gal (β1-3) NNa3Ab3GNb Sialyl Lewis c GlcNAc (β) 54 Neu5Ac (α1-3) Gal (β 1-3)NNa3Ab3ANa SiT antigen GalNAc (α)

TABLE 2 Number of positive samples having binding signals above the 97%percentile of healthy population. Glycan Result MS Healthy Glc (α)Positive 19/42 (45%) 2/44 (4.5%) Negative 23/42 (55%) 42/44 (96%)   Glc(α 1-4) Glc (α) Positive 20/42 (48%) 2/44 (4.5%) Negative 22/42 (52%)42/44 (96%)   Glc (α 1-4) Glc Positive 25/42 (60%) 3/44 (6.8%) (α) ORGlc (α) Negative 17/42 (40%) 41/44 (93%)  

TABLE 3 Number of positive samples having binding signals above the 80%percentile of “stable” MS population. Glycan Result Attack StableHealthy Glc (α) Positive 15/18 (83%)  5/24 (21%) 2/44 (4.5%) Negative 3/18 (17%) 19/24 (79%) 42/44 (96%)   Glc (α 1-4) Positive 13/18 (72%) 5/24 (21%) 2/44 (4.5%) Glc (α) Negative  5/18 (28%) 19/24 (79%) 42/44(96%)   Glc (α 1-4) Positive 16/18 (89%)  7/24 (29%) 2/44 (4.5%) Glc (α)OR Negative  2/18 (11%) 17/24 (71%) 42/44 (96%)   Glc (α)

TABLE 4 No. LinearCode ™ 1 A[3S]b 2 Aa 3 Aa3Ab4GNb 4 Aa4Ab4Gb 5 Ab 6Ab3(GNb6)ANa 7 Ab3ANa 8 Ab3GNb 9 Ab4(Fa3)GNb 10 Ab4Gb 11 Ab4GNb 12 Ab6Ab13 ANa 14 ANb 15 Fa 16 Fa2Ab 17 Fb 18 Ga 19 Ga4Ga 20 Ga4Ga 21 Gb 22Gb3Gb 23 Gb4Gb4Gb 24 GN[6S]b 25 GNa 26 GNb 27 GNb3Ab4Gb 28 GNb3ANa 29GNb4GNb 30 GNb6ANa 31 Ha 32 Lb 33 Ma 34 Ma3(Ma6)Mb 35 Ma3Ma 36 Ma6Ma 37Mb 38 Mb4Gb 39 NNa3Ab3(Fa4)GNb 40 NNa3Ab3ANa 41 NNa3Ab3GNb 42 NNa3Ab4Gb43 NNa3Ab4GNb 44 NNa6Ab4GNb 45 OH 46 Ra 47 NNa3Ab4(Fa3)GNb 48 Ub 49 Xa50 Xb

TABLE 5 Table 5. Saccharides displayed on glycan array and level ofanti-glycan Linear Ab Binding Relative Glycan No. Glycan Code ®¹¹(RFU)^(a) Ab 0 pAP — NA NA 1 Gal (α) Aa 181,069 10 2 Gal (β) Ab 119,03414 3 Gal (β 1-3) Ab3Ana 52,853 GalNAc (α) 4 Gal (β 1-3) Ab3GNb 58,239GlcNAc (β) 5 Gal (β 1-4) Ab4Gb 92,170 Glc (β) 6 Gal (β 1-6) Ab6Ab151,313 11 Gal (β) 7 GalNAc (α) ANa 64,429 8 GalNAc (β) ANb 57,832 10Fuc (α) Fa 47,727 11 Fuc (β) Fb 63,782 12 Glc (α) Ga 109,091 13 Glc (α1-4) Glc (α) Ga4Ga 80,024 14 Glc (α 1-4) Glc (β) Ga4Gb 127,594 13 15 Glc(β) Gb 112,513 16 Glc (β 1-4) Glc (β) Gb4Gb 239,830 9 17 Glc (β 1-4) GlcGb4Gb4 284,361 7 (β 1-4) 18 Glc (β 1-4) Glc Gb4Gb4 311,235 5 (β 1-4) 19Glycerol Glycerol 52,884 20 GlcNAc (α) GNa 1,031,130 1 21 GlcNAc (β) GNb311,341 4 22 GlcNAc (β 1-3) GNb3A 294,624 6 GalNAc 23 GlcNAc (β 1-4)GNb4G 433,604 3 GlcNAc 24 L-Rha (α) Ha 662,337 2 25 GalA (β) Lb 96,80126 Man (α) Ma 83,647 27 Man (β) Mb 77,533 28 Neu5Ac (α) NNa 52,028 29L-Araf (α) Ra 51,230 30 GlcA (β) Ub 56,719 31 X(α) Xa 55,806 32 X(α) Xb78,776 33 Gal (β1-3) [GlcNAc Ab3(GN 84,080 34 Gal (β 1-4) Ab4GNa 143,03612 GlcNAc (α) 36 Gal (β1-3) Gal (β1- Aa3Ab4 268,549 8

1. A method of diagnosing multiple sclerosis in a subject, the methodcomprising providing a test sample from a subject; detecting in saidtest sample at least one antibody selected from the group consisting ofan anti-Glc (α) antibody, an anti-Glc (α 1-4) Glc (α) antibody, ananti-Glc (α 1-4) Glc (β) antibody, an anti-Glc (β) antibody, an anti-Gal(β) antibody; an anti-Glc (β 1-4) Glc (β 1-4) Glc (β) antibody, ananti-GlcNAc (β 1-4) GlcNAc (β) antibody, an anti-L-Araf (α) antibody, ananti-L-Rha (α) antibody, an anti-Gal (β 1-3) [GlcNAc (β 1-6)] GalNAc(α)antibody, an anti-Gal (β 1-4) GlcNAc (α)antibody, an anti-Gal (β 1-3)GalNAc (α) antibody, an anti-Gal (β 1-3) GlcNAc (β) antibody, ananti-GlcA (β) antibody, an anti-GlcA (β) antibody, and an anti-Xyl (α)antibody; and comparing the levels of said at least one antibody in saidtest sample to the levels of said at least one antibody in a controlsample, wherein said control sample is selected from the groupconsisting of one or more individuals that have multiple sclerosissymptoms and have a known multiple sclerosis status, and one or moreindividuals that do not show multiple sclerosis symptoms, therebydiagnosing multiple sclerosis in said subject.
 2. The method of claim 1,wherein said method comprises detecting an anti-Glc (α) antibody in saidtest sample; and comparing the levels of said antibody in said testsample to said control sample.
 3. The method of claim 1, wherein saidmethod comprises detecting an anti-Glc (α 1-4) Glc (α) antibody in saidtest sample; and
 4. The method of claim 1, wherein said method comprisesdetecting an anti-Glc (α 1-4) Glc (α) antibody and an anti-Glc (α)antibody in said test sample; and comparing the level of said antibodiesin said test sample to said control sample.
 5. The method of claim 1,wherein said control sample consists essentially of a population of oneor more individuals that have multiple sclerosis symptoms with a knownmultiple sclerosis status.
 6. The method of claim 1, wherein said testsample is a biological fluid.
 7. The method of claim 6, wherein saidbiological fluid is whole blood, serum, plasma, spinal cord fluid,urine, or saliva.
 8. The method of claim 1, wherein said biologicalfluid is serum.
 9. The method of claim 1, wherein said subject is afemale.
 10. The method of claim 1, wherein said subject is a male. 11.The method of claim 1, wherein said at least one antibody is an IgM typeantibody.
 12. The method of claim 1, wherein said at least one antibodyis an IgA type antibody.
 13. The method of claim 1, wherein said atleast one antibody is an IgG type antibody.
 14. The method of claim 2,wherein said anti-Glc (α) antibody is an IgM type antibody.
 15. Themethod of claim 3, wherein said anti-Glc (α 1-4) Glc (α) antibody is anIgM type antibody.
 16. The method of claim 1, wherein said diagnosis isan early diagnosis of multiple sclerosis.
 17. The method of claim 1,wherein said control sample is determined using an Expanded DisabilityStatus Scale (EDSS) assessment or a Magnetic Resonance Imaging (MRI)assessment.
 18. The method of claim 1, wherein said control sample isdetermined using an Expanded Disability Status Scale (EDSS) assessment.19. The method of claim 1, wherein said method comprises detecting atleast two of said antibodies.
 20. The method of claim 1, wherein saidmethod comprises detecting at least four of said antibodies.
 21. Themethod of claim 1, wherein said method comprises detecting at least sixof said antibodies.
 22. A method of diagnosing a multiple sclerosisexacerbation in a subject, the method comprising providing a test samplefrom a subject; detecting an anti-Glc (α) IgM type antibody or ananti-Glc (α 1-4) Glc (α) IgM type antibody in said test sample; andcomparing the levels of said antibody in said test sample to a controlsample, wherein said control sample is derived from one or moreindividuals whose multiple sclerosis status is known, thereby diagnosingmultiple sclerosis exacerbation in said subject.
 23. The method of claim22, wherein said method comprises detecting an anti-Glc (α) IgM typeantibody in said test sample; and comparing the levels of said antibodyin said test sample to said control sample.
 24. The method of claim 22,wherein said method comprises detecting an anti-Glc (α 1-4) Glc (α) aIgM type antibody in said test sample; and comparing the levels of saidantibody in said test sample to said control sample.
 25. The method ofclaim 22, wherein said method comprises detecting an anti-α-Glucose IgMtype antibody and an anti-Glc (α 1-4) Glc (α) a IgM type antibody insaid test sample; and comparing the levels of said antibodies in saidtest sample to said control sample.
 26. The method of claim 22, whereinsaid control sample consists essentially of a population of one or moreindividuals in remission multiple sclerosis status that do not showsymptoms of a multiple sclerosis exacerbation, and a multiple sclerosisexacerbation is diagnosed in said subject if more anti-Glc (α) antibodyor anti-Glc (α 1-4) Glc (α) antibody is present in said test sample thanin said control sample.
 27. The method of claim 22, wherein said controlsample consists essentially of a population of one or more individualsthat their multiple sclerosis status in exacerbation, and show symptomsof a multiple sclerosis exacerbation, and a multiple sclerosisexacerbation is diagnosed in said subject if similar anti-Glc (α)antibody or anti-Glc (α 1-4) Glc (α) antibody levels is present in saidtest sample and in said control sample.
 28. The method of claim 22,wherein said test sample is a biological fluid.
 29. The method of claim28, wherein said biological fluid is whole blood, serum, plasma, spinalcord fluid, urine, or saliva.
 30. The method of claim 28, wherein saidbiological fluid is serum.
 31. The method of claim 22, wherein saidsubject is a female.
 32. The method of claim 22, wherein said subject isa male.
 33. The method of claim 22, wherein said diagnosis is an earlydiagnosis of multiple sclerosis exacerbation.
 34. The method of claim22, wherein said subject has been treated by subcutaneous administrationof interferon beta.
 35. The method of claim 22, wherein said subject hasbeen treated by subcutaneous administration of glitamerer acetate.
 36. Amethod for assessing multiple sclerosis disease severity in a subject,the method comprising providing a test sample from a subject;determining whether said test sample contains an anti-α Glucose IgM typeantibody or an anti-Glc (α 1-4) Glc (α) IgM type antibody; and comparingthe level of said at least one antibody in said test sample to a controlsample, wherein said control sample is derived from one or moreindividuals whose multiple sclerosis disease severity is known. therebyassessing of multiple sclerosis severity in said subject.
 37. The methodof claim 36, wherein said method comprises detecting an anti-Glc (α) IgMtype antibody in said test sample; and comparing the levels of saidantibody in said test sample to said control sample.
 38. The method ofclaim 35, wherein said method comprises detecting an anti-Glc (α 1-4)Glc (α) IgM type antibody in said test sample; and comparing the levelsof said antibodies in said test sample to said control sample.
 39. Themethod of claim 35, wherein said method comprises detecting an anti-Glc(α 1-4) Glc (α) IgM type antibody and an anti-Glc (α) IgM type antibodyin said test sample; and comparing the level of said antibodies in saidtest sample to said control sample.
 40. The method of claim 36, whereinsaid control sample consists essentially of a population of one or moreindividuals whose multiple sclerosis disease severity is defined byExpanded Disability Status Scale (EDSS), changes in an EDSS score, or aMagnetic Resonance Imaging (MRI) assessment.
 41. The method of claim 36,wherein said test sample is a biological fluid.
 42. The method of claim41, wherein said biological fluid is whole blood, serum, plasma, spinalcord fluid, urine, saliva.
 43. The method of claim 41, wherein saidbiological fluid is serum.
 44. The method of claim 36, wherein saidsubject is a female.
 45. The method of claim 36, wherein said subject isa male.
 46. The method of claim 36, further comprising selecting atherapeutic agent for treating multiple sclerosis, the method comprisingdetermining whether said test sample contains anti Glucose α antibody;and selecting a therapeutic agent and dosage regimen based on therelative levels of said antibody in said subject sample and said controlsample.
 47. The method of claim 46, wherein said method furthercomprises determining whether said test sample contains an anti-Glc (α1-4) Glc (α) antibody; and comparing the levels of said an anti-Glc (α1-4) Glc (α) antibody in said test sample to levels of antibody in acontrol sample consisting essentially of one or more individuals whosemultiple sclerosis status is known.
 48. A kit for diagnosing symptomsassociated with multiple sclerosis, the kit comprising: a first reagentthat specifically detects an anti-Glc (α 1-4) Glc (α) antibody; a secondreagent that specifically detects an anti-Glc (α 1-4) Glc (α) antibody;and directions for using said kit.
 49. The kit of claim 48, furthercomprising a reagent that specifically detects an IgM type antibody.